Covid-19 Data Analysis in Tarakan with Poisson Regression and Spatial Poisson Process

Abstract

COVID-19 entered Indonesia in March 2020 and included North Kalimantan Province, Tarakan. COVID-19 cases have outspread in Tarakan. The cause of the outspread and the patterns were not known yet. One relevant approach was to use Generalized Linear Models. The two methods are Poisson Regression and Stochastic with Spatial Poisson Process. The variables used were rainfall, population density, and temperature in each village in Tarakan. The Poisson Regression analysis founds that only one factor affected temperature. Then, the results were refined with the Spatial Poisson Process, where in addition to the influencing factors also, the distribution patterns are obtained. The analysis showed that the pattern of case distribution was included in the non-homogeneous Poisson process criteria. Then the model of the case density intensity was obtained using regression. From the model, it was known that the covariate variables significantly influence rainfall and temperature. Compared with general Poisson regression analysis, the results showed that only the average temperature variables had a significant effect. Thus, a better method was used, namely the Spatial Poisson Process. It was also shown by the two models' AIC values, where the AIC value of the Spatial Poisson Process model was smaller than the Poisson Regression.